Human percepts the interactions or “Gestalt” as well as each element of object; and then recognizes or feels it. The processes are entirely ambiguous in the meaning of individual differences in recognition and feeling. The interactions and ambiguity are also pervasive features of kansei. If we can handle the features adequately in kansei engineering methodology; we will obtain more significant information on the interactive relations between customer wants and kansei words; product design attributes and kansei; and so on.

In this respect; rough set theory provides a useful approach to kansei engineering methodology which approximates ambiguous concept and then identifies the interactive relations between elements in the processes in the form of IF-THEN decision rules. We identified the kansei as indefinable rough set in the framework of rough set theory and proposed a rough set model which represents probabilistically the kansei or human evaluation data with much ambiguity; and derives decision rules of causes and effect relation in the processes (Nishino; 2005a). Moreover; we found that the proposed rough set model is useful in application to kansei product designs (Nishino; Nagamachi and Sakawa; 2006b).
The purpose of the paper is to propose an extended rough set model for extracting.

Decision rules from combined kansei evaluation data and an application method of the model to strategically extract design attributes matched with customer kansei; and more we applied our method to ball pen design. Proposed application method identified the direction of customer wants in principal component space; development concepts; and design specifications using the rough set model.